Machine Learning with Python

Course ID
MLP
Department
Software Engineering
Campus
1 Cornhill
Level
Certificate
Method
Lecture, Project
Duration
3 Months
Machine Learning with Python
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Optional Add-on Programs

Job Guarantee Program

The Job Guarantee program is available only to candidates who enroll in Option 6 (Project and Industrial Training and Paid Internship Program + Pro Plan). It is important to note, however, that the Job Guarantee program has its own selection criteria, so not everyone may be considered for the program. To learn more about the Job Guarantee program, please visit Job Guaranteed Software Courses

Pro Plan Card

LSET PRO PLAN

Are you eager to enter the workforce fully prepared? Look no further than our LSET PRO PLAN! This is an add-on program that you can select during your course enrolment, it offers a personalised learning experience that helps you succeed in your course, build your technical portfolio, and advance your professional journey.
Curious about how to embark on this journey? Simply “click” here to learn more and kickstart your professional development with us!

Machine Learning has become a vast field in computer science. It is basically getting things done by the computers without explicitly programming them. It has given us so many technologies self-driving car, speech recognition, web recommendation engines, etc.

Apply now to become a professional Machine learner

Are you looking for corporate training?
We tailor our courses to meet the specific needs of your team. If you would like to discuss your training requirements, please email [email protected] today.
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Course feature icons

Prerequisites have been met

Options
Course Pack
Add-On
Duration
Options
Option 1
Course Pack
Machine Learning (Prior Knowledge of Python Required)
Add-On
Duration
3 Months
Options
Option 2
Course Pack
Machine Learning (Prior Knowledge of Python Required)
Add-On
Project (Online)
Duration
5 Months
Options
Option 3
Course Pack
Machine Learning (Prior Knowledge of Python Required)
Add-On
Project (Online) &
Industrial Training and Paid Internship Program (Remote)
Duration
12 Months

Prerequisites have not been met

Options
Course Pack
Add-On
Duration
Options
Option 1
Course Pack
Python + Machine Learning
Add-On
Duration
4 Months
Options
Option 2
Course Pack
Python + Machine Learning
Add-On
Project (Online)
Duration
6 Months
Options
Option 3
Course Pack
Python + Machine Learning
Add-On
Project (Online) &
Industrial Training and Paid Internship Program (Remote)
Duration
13 Months

Tuition Fees

Options
Course Pack
Home (UK) & International
Online
Home (UK)
Classroom
International
Classroom
Options
Option 1
Course Pack
Machine Learning (Prior Knowledge of Python Required) – (3 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £1,860
Pay Per Module: 
Number of Module: 2
Per Module Fee: £1,116
Home Classroom
Pay Upfront (with 20% Disc) : £3,660
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,196
International Classroom
£6,060
Options
Option 2
Course Pack
Machine Learning (Prior Knowledge of Python Required) + Project (Online) – (5 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £3,060
Pay Per Module: 
Number of Module: 3
Per Module Fee: £1,224
Home Classroom
Pay Upfront (with 20% Disc) : £6,060
Pay Per Module: 
Number of Module: 3
Per Module Fee: £2,424
International Classroom
£8,460
Options
Option 3
Course Pack
Machine Learning (Prior Knowledge of Python Required) + Project (Online) + Industrial Training and Paid Internship Program (Remote) – (12 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £6,060
Pay Per Module:
Number of Module: 10
Per Module Fee: £728
Home Classroom
Pay Upfront (with 20% Disc) : £13,260
Pay Per Module: 
Number of Module: 10
Per Module Fee: £1,592
International Classroom
£15,660
Options
Option 4
Course Pack
Machine Learning (Prior Knowledge of Python Required) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £2,340
Pay Per Module:
Number of Module: 2
Per Module Fee: £1,404
Home Classroom
Pay Upfront (with 20% Disc) : £4,140
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,484
International Classroom
£6,540
Options
Option 5
Course Pack
Machine Learning (Prior Knowledge of Python Required) + Project (Online) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £3,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,416
Home Classroom
Pay Upfront (with 20% Disc) : £6,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,616
International Classroom
£8,940
Options
Option 6
Course Pack
Machine Learning (Prior Knowledge of Python Required) + Project (Online) + Industrial Training and Paid Internship Program (Remote) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £6,540
Pay Per Module:
Number of Module: 10
Per Module Fee: £785
Home Classroom
Pay Upfront (with 20% Disc) : £13,740
Pay Per Module:
Number of Module: 10
Per Module Fee: £1,649
International Classroom
£16,140

Prerequisites have not been met

Options
Course Pack
Home (UK) & International
Online
Home (UK)
Classroom
International
Classroom
Options
Option 1
Course Pack
Python + Machine Learning – (4 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £2,700
Pay Per Module: 
Number of Module: 3
Per Module Fee: £1,080
Home Classroom
Pay Upfront (with 20% Disc) : £5,100
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,040
International Classroom
£9,000
Options
Option 2
Course Pack
Python + Machine Learning + Project (Online) – (6 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £3,900
Pay Per Module: 
Number of Module: 5
Per Module Fee: £936
Home Classroom
Pay Upfront (with 20% Disc) : £7,500
Pay Per Module: 
Number of Module: 5
Per Module Fee: £1,800
International Classroom
£11,880
Options
Option 3
Course Pack
Python + Machine Learning + Project (Online) + Industrial Training and Paid Internship Program (Remote) – (13 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £6,900
Pay Per Module:
Number of Module: 10
Per Module Fee: £828
Home Classroom
Pay Upfront (with 20% Disc) : £14,700
Pay Per Module: 
Number of Module: 10
Per Module Fee: £1,764
International Classroom
£20,520
Options
Option 4
Course Pack
Python + Machine Learning + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £3,180
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,272
Home Classroom
Pay Upfront (with 20% Disc) : £5,580
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,232
International Classroom
£9,576
Options
Option 5
Course Pack
Python + Machine Learning + Project (Online) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £4,380
Pay Per Module:
Number of Module: 5
Per Module Fee: £1,052
Home Classroom
Pay Upfront (with 20% Disc) : £7,980
Pay Per Module:
Number of Module: 5
Per Module Fee: £1,916
International Classroom
£12,456
Options
Option 6
Course Pack
Python + Machine Learning + Project (Online) + Industrial Training and Paid Internship Program (Remote) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £7,380
Pay Per Module:
Number of Module: 10
Per Module Fee: £886
Home Classroom
Pay Upfront (with 20% Disc) : £15,180
Pay Per Module:
Number of Module: 10
Per Module Fee: £1,822
International Classroom
£21,096
   Note: Our Industrial Training and Internship program includes a guaranteed 6 months paid internship (from 10 hours to 40 hours per week) with a technology company. Due to visa restrictions, some international students may not be able to participate in this program.

Machine Learning, refers to a process of analysing data for training or building models. It is everywhere; from Amazon product recommendations to self-driven cars, it has great value throughout. As per the latest survey, the machine learning market is expected to grow by 43% by 2024. This revolution has greatly enhanced the demand for machine learning professionals.

Machine learning jobs have had a significant growth rate of 75% in the last four years, and the industry is rapidly continuously. In this course, we will work on your core skills like Machine Learning, and Learn specific topics like NLP, Reinforcement Learning, Deep Learning, and a lot more.

Career Perspective

Machine Learning is a process of analysing data for training and building models. ML is just everywhere; from self-driven cars to Amazon product recommendations, it holds great value throughout. As per the latest survey, the global machine learning market is expected to grow by 43% by the year 2024. This revolution has increased the demand for machine learning professionals to a huge extent.

Machine learning and Artificial Intelligence jobs have had a significant growth rate of 75% in the last four years, and the industry is growing rapidly. The average salary of an ML professional is £52,000. A career in the Machine learning domain offers excellent growth, job satisfaction, insanely high salary, but it is a complex and challenging process.

Technologies Covered

Python programming language: It is an interpreted high-level programming language used for web development, machine learning, AI, ML, and a lot more. It provides a clear approach to programmers to write a clear and logical approach.

Pandas is a software library used for Data Analysis and manipulation. It offers operations and data structure and operations for manipulating time series and numerical tables.

NumPy: it is a python library that consists of the multidimensional array and a collection of mathematical functions to operate on this array

Matplotlib is a python library that makes matplotlib work like MATLAB. It provides an object-oriented API for inculcating plots into the application using GUI.

Plotly is an open-source plotting library that supports a wide range of scientific, financial, and geographical use cases.

SciKit-Learn is a Machine Learning library for the Python programming language. It features various regression, classification, and clustering algorithms.

Complementary Workshops

Git Management

Agile Project Management

Agile Project Management

Team Building

Personality Development

Interview Preparation

Course Information

Course Intakes

September

End: December

January

End: April

May

End: August

Entry Criteria

  • Students must have at least high school knowledge in maths and must be willing to learn Machine Learning.
  • Ability to work in Group
  • If a potential student’s first language is not English, they must also reach the English Language requirements of either any one of the following - IELTS 5.5 or NCC Test or GCE “O” Level English C6.
  • Have access to personal laptop

Course Highlights

  • Hands-on practice
  • Industry-standard Project development
  • Learn from experts
  • Interactive teaching
  • Full lifetime access
  • Access on mobile
  • Certificate of completion

Evaluation Criteria

  • 18 Coding exercises
  • 5 Assignments
  • 5 Quizzes
  • Capstone Project
  • Group activities
  • Presentations

Learning Objectives

  • Anyone interested in Machine Learning.
  • College students who want to build a career in Data Science.
  • Data analysts who are willing to level up in Machine Learning.
  • People who are dissatisfied with their job and are willing to become Data scientists.
  • People who are willing to add value to their business by using robust Machine Learning tools.
  • Students must have at least high school knowledge in maths and must be willing to learn Machine Learning.
  • Any intermediate-level person who has an idea about the basics of machine learning, classical algorithms like logistic regression or linear regression, but who wants to learn more about it and explore different fields of Machine Learning.

Weekday Batches

Batch 01
09:00 am – 11:00 am
(Mon, Wed)

Batch 02
12:00 pm – 02:00 pm
(Mon, Wed)

Batch 03
03:00 pm – 05:00 pm
(Mon, Wed)

Batch 04
05:30 pm – 07:30 pm
(Mon, Wed)

Weekend Batches

Batch 01
08:00 am – 10:00 am
(Sat, Sun)

Batch 02
10:00 am – 12:00 pm
(Sat, Sun)

Hands-on Workshops

Interview Preparation

CV Preparation

Personality Development

Join Now

Join the Machine Learning Advanced Certificate course to start creating ML algorithms in Python and R with data science educators. Become a seasoned machine learning expert with LSET’s practical and project-based learning environment.

Apply Now

Course Content

Browse the LSET interactive and practical curriculum

Introduction

>> Course Introduction >> How to make the best of this course >> GIT Introduction and Setup >> Data Pre-processing

Regression

>> Scikit-Learn >> EDA >> Correlation Analysis and Feature Selection >> Linear Regression with Scikit-Learn
>> Five Steps Machine Learning Process >> Robust Regression >> Evaluate Regression Model Performance >> Multiple Regression 1
>> Multiple Regression 2 >> Regularised Regression >> Polynomial Regression >> Dealing with Non-linear Relationships
>> Feature Importance >> Data Preprocessing >> Variance-Bias Trade Off >> Learning Curve
>> Cross Validation >> CV Illustration

Classification

>> Logistic Regression >> Introduction to Classification >> Understanding MNIST >> SGD
>> Performance Measure and Stratified k-Fold >> Confusion Matrix >> Precision >> Recall
>> f1 >> Precision Recall Tradeoff >> Altering the Precision Recall Tradeoff >> ROC

Support Vector Machine (SVM)

>> Support Vector Machine (SVM) Concepts >> Linear SVM Classification >> Polynomial Kernel >> Radial Basis Function
>> Support Vector Regression

Tree

>> Introduction to Decision Tree >> Training and Visualising a Decision Tree >> Visualizing Boundary >> Tree Regression, Regularisation and Over Fitting
>> End to End Modelling

Ensemble Machine Learning

>> Ensemble Learning Methods Introduction >> Bagging >> Random Forests and Extra-Trees >> AdaBoost
>> Gradient Boosting Machine >> XGBoost Installation >> XGBoost >> Ensemble of Ensembles Part 1 >> Ensemble of Ensembles Part 2

Unsupervised Learning: Dimensionality Reduction

>> Dimensionality Reduction Concept >> PCA Introduction >> Project Wine >> Kernel PCA
>> Kernel PCA Demo >> LDA vs PCA

Deep Learning

>> Estimating Simple Function with Neural Networks >> Neural Network Architecture >> Motivational Example – Project MNIST >> Binary Classification Problem
>> Natural Language Processing – Binary Classification

Appendix A1: Foundations of Deep Learning

>> Introduction to Neural Networks >> Differences between Classical Programming and Machine Learning >> Learning Representations >> What is Deep Learning
>> Learning Neural Networks >> Building Block Introduction >> Tensors >> Tensor Operations
>> Tensor Operations >> Gradient Based Optimization >> Getting Started with Neural Network and Deep Learning Libraries >> Categories of Machine Learning
>> Over and Under Fitting >> Machine Learning Workflow

Computer Vision and Convolutional Neural Network (CNN)

>> Outline >> Neural Network Revision >> Motivational Example >> Visualizing CNN
>> Understanding CNN >> Layer – Input >> Layer – Filter >> Activation Function
>> Pooling, Flatten, Dense >> Training Your CNN 1 >> Training Your CNN 2 >> Loading Previously Trained Model
>> Model Performance Comparison >> Data Augmentation >> Transfer Learning >> Feature Extraction

*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.

Having Doubts?

Contact LSET Counsellor

We love to answer questions, empower students, and motivate professionals. Feel free to fill out the form and clear up your doubts related to our Machine learning Course

Best Career Paths

Machine Learning Engineer

A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models.

Business Intelligence (BI) Developer

A business intelligence developer is an engineer that's in charge of developing, deploying, and maintaining BI interfaces. Those include query tools, data visualisation and interactive dashboards, ad hoc reporting, and data modeling tools.

Data Scientist

A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyses it for better understanding about how the business performs, and to build AI tools that automate certain processes within the company.

Human-Centered Machine Learning Designer

A human-centered machine learning designer works to create technology-based programs, applications, and devices that ultimately solve the issues experienced by people using the technology.

Computational Linguist

Computational linguistics is the scientific and engineering discipline concerned with understanding written and spoken language from a computational perspective, and building artifacts that usefully process and produce language, either in bulk or in a dialogue setting.

Software Developer

Software engineers design, develop, and test software and applications for computers. The main duties and responsibilities of software engineers include directing and participating in programming activities, monitoring, and evaluating system performance, and designing and implementing new programs and features.

Top Companies Hiring

Amazon

Amazon

APPLE

Apple

BANK OF AMERICA

Bank of America

Google

Google

Microsoft

MICROSOFT

Faculties & Mentors

Mayur Ramgir

Mayur Ramgir

Mentor Panel

Dr. Sebelan Danishvar

Reasons to learn Machine learning

  • Amazing job opportunities as the demand for Machine learning professionals is increasing at a high pace
  • Machine learning enhances your decision-making power and it is easy to understand and learn.
  • As per the survey average salary of machine learning, professionals is £52,000.
  • Less completion in the field as there is an alarming gap between the demand and supply of machine learning professionals.

Who should apply for this course?

  • Students who are willing to take machine learning as a career and want proficiency in this domain.
  • It is also a great course for working professionals who want to shift to the Machine learning domain.
  • Students who want to achieve a great and successful career.
  • It is the best course for students who want to build a strong foundation in Machine learning.

About Course

If you are willing to build your career in Machine Learning. Then this course is exclusively for you!

This course will help you learn about complex theory, algorithms, and coding libraries in a much simpler way. We will be your guide and help you ace into the World of Machine Learning.

With every tutorial, you will learn new skills and improve your understanding of this lucrative sub-field of Data Science. This course is exciting and challenging, but at the same time, we dive deep into the concepts of Machine Learning.

  • This course enhances your machine learning skills to help you land your dream job.
  • Through this course, you will get in-depth information about python and its machine learning library.
  • Career guidance and doubt resolving sessions with industry experts.
  • Learning through hands-on experience and live project development.
  • You will learn in detail about NLP, Reinforcement Learning, and Deep Learning, and a lot more.

The Course Provides Shared Expertise by

LSET Trainers

LSET Trainers

Industry Experts

Industry Experts

Top Employers

Top Employers

Skills You will Gain

  • Computer science fundamentals
  • Programming
  • Math and statistics
  • Machine Learning Workflow
  • Deep learning
  • Problem solving
  • Artificial intelligence
  • Software engineering
  • System Design
  • Classification
  • Neural Network Architecture
  • Polynomial Kernel

Complete Learning Experience

This course provides a hands-on, guided learning experience to help you learn the fundamentals practically.
  • We constantly update the curriculum to include the latest releases and features.
  • We focus on teaching the industry's best practices and standards.
  • We let you explore the topics through guided hands-on sessions.
  • We provide industry professional mentor support to every student.
  • We give you an opportunity to work on real world examples.
  • Work with hands-on projects and assignments.
  • We help you build a technical portfolio that you can present to prospective employers.

Reasons to Choose LSET

  • Interactive live sessions by industry experts.
  • Practical classes with project-based learning with hands-on activities.
  • International learning platform to promote collaboration and teamwork.
  • Most up-to-date course curriculum based on current industry demand.
  • Gain access to various e-learning resources.
  • One-to-one attention to ensure maximum participation in the classes.
  • Lifetime career guidance to get the students employed in good companies.
  • Free lifetime membership to the LSET Alumni Club

What Will Be Your Responsibilities?

  • Work creatively in a problem-solving environment.
  • Ask questions and participate in class discussions.
  • Work on assignments and quizzes promptly.
  • Read additional resources on the course topics and ask questions in class.
  • Actively participate in team projects and presentations.
  • Work with the career development department to prepare for interviews
  • Respond promptly to the instructors, student service officers, career development officers, etc.
  • And most importantly, have fun while learning at LSET.
Your Responsibilities

How Does Project-Based Learning Work?

LSET project-based learning model allows students to work on real-world applications and apply their knowledge and skills gained in the course to build high-performing industry-grade applications. As part of this course, students learn agile project management concepts, tools, and techniques to work on the assigned project collaboratively. Each student completes project work individually but is encouraged to enhance their solution by collaborating with their teammates.

Following are the steps involved in the LSET’s project-based learning;

  1. Step 1: Project Idea Discussion

    In this step, students get introduced to the problem and develop a strategy to build the solution.

  2. Step 2: Build Product Backlog

    This step requires students to enhance the existing starter product backlog available in the project. This helps students to think about real-life business requirements and formulate them in good user stories.

  3. Step 3: Design Releases and Sprints

    In this step, students define software releases and plan sprints for each release. Students must go through sprint planning individually and learn about story points and velocity.

  4. Step 4: Unit and Integration Tests

    In this step, students learn to write unit tests to ensure every application part works fine.

  5. Step 5: Use CICD to Deploy

    In this step, students learn to use CICD (Continuous Integration Continuous Delivery) pipeline to build their application as a docker image and deploy it to Kubernetes.

Capstone Project

LSET gives you an opportunity to work on the real world project which will greatly help you to build your technical portfolio

Project Topic: Online Banking

London has been a leading international financial centre since the 19th century. In recent years, London has seen many FinTech start-ups and significant innovations in the banking sector. This project aims to introduce students to the financial industry and technologies used to handle billions of daily transactions. As part of this project, students will learn the current technological advances and build up their knowledge to start a simple banking application. This application uses agile project management practices to build basic functionality. Students will be presented with user stories to create the initial project backlog. Students need to enhance this backlog by adding more relevant user stories and working on them.

LSET emphasises project-based learning as it allows the students to master the course content by going through near real-world work experience. LSET projects are carefully designed to teach the industry-required skills and mindset. It motivates the students on various essential aspects like learning to work in teams, improving communication with peers, taking the initiative to look for innovative solutions, enhancing problem-solving skills, understanding the end user requirements to build user-specific products, etc.

Capstone Projects build students’ confidence in handling projects and applying their newly learned skills to solve real-world problems. This allows the students to reflect upon their learning and find the opportunity to get the most out of the course. Learn more about Capstone Projects here.

Learning Outcome

  • Students will learn to work in an agile environment
  • Students will learn the agile project management terms used in the industry, like product backlog, user stories, story points, epics, etc.
  • Students will learn to use a Git repository and understand the concepts like commit, pull, push, branch, etc.
  • Students will learn to communicate in a team environment and effectively express their ideas.

Guidance and Help

A dedicated project coordinator who can mentor students on the process will be assigned to this project. Students can also avail of the instructor’s hours as and when needed. LSET may get an industry expert with subject-specific experience to help students understand the industry and its challenges.

Execution Process

This project will be carried out in steps. Each step teaches students a specific aspect of the subject and development paradigm. Following are the steps students will follow to complete this project.

Step 1: Project Introduction Self Study [6 days]

In the first step, students will learn about the financial industry and review the project introduction documentation to build up the subject knowledge. This is a self-learning stage; however, instructor hours are available if required.

Step 2: Project Build-up and Environment Setup [2 days]

In this step, students are required to follow the project guide to set up the development environment. The project document guides students to find and connect to the LSET Git repository and install the necessary libraries or tools.

Step 3: Product Backlog and Sprint Planning [2 days]

In this step, students will use the existing product backlog and enhance it per their project scope. Students can seek help from the project coordinator and the instructor. The project coordinator will help students do sprint planning and assign story points to the stories. This process is meant to give students real-world work environment experience. Students can consider this a mock exercise on agile project management practices.

Step 4: User Stories Execution and Development [12 days]

Students will work on the user stories identified in the Step 3 process in this step. Students will write code and algorithms to complete the development objectives. The project coordinator will be available to help students to guide them on the development and answer any questions they may have. Students can also discuss this with the instructor.

Step 5: Testing, Deployment and Completion [5 days]

In this step, students will test and deploy the application to the cloud environment. Students will experience the deployment process in the cloud and learn the best practices. After the successful deployment, students will present their project to the instructor and the external project reviewer. Feedback will be given to the students. Students will have one week to work on the feedback and submit the final copy of the project, which will be sent to the external examiner for evaluation.

Project Presentation

LSET emphasises preparing students for the work environment by allowing them to learn the required soft skills. After completing the project, students must present their work to the instructor and an invited project reviewer panel. Please note that the assigned external examiner will not be part of this panel and hence will not know about the students. This ensures an unbiased assessment by the external examiner. This exercise aims to allow students to experience an environment they may face in their actual job. Also, it gives them a chance to get feedback from industry experts who can guide students on various parts of the project. This will help students to learn and fix anything they find necessary in their project. This ensures quality output and allows students to learn about industry requirements.

The instructor and the project reviewer panel will assess the students on the following;

Project Repository on GitHub [10 points]: The instructor will ensure that the students have uploaded the project repository to the LSET’s GitHub account per the guidelines in the project requirement documentation. Full points will be awarded if the repository is appropriately set up per the instructions.

Presentation Skills [20 points]: Students must present their work in the given timeframe. Full points will be awarded if students cover everything needed to deliver their work in the given timeframe.

Communication Skills [20 points]: Students must present their work in a manner understandable by all the participants. More focus will be given to how students communicate, not the language. Full points will be awarded if students can share their work correctly.

Evaluation Criteria

LSET promotes a transparent and unbiased evaluation process. All the external examiners will follow a set process to grade students. No student’s personal or identifying information will be shared with the external examiners, so they will not know about the person they are grading. They will only get the project files and grading guidelines to follow. This will ensure equal quality standards across the institute.

Following are some critical areas the LSET external examiners will be grading on.

Project Documentation [10 points]: Project documentation is filed correctly with the information which can be used to understand the project work. Students can use the supplied project documentation template to fill up the data. External examiner to confirm if all the information is filled up. Full points will be awarded if all the sections are covered.

Project Structure [10 points]: Students must follow the proper structure while developing their projects. This structure is being taught and covered in the project requirement documentation. External examiner to confirm if the project files are correctly structured. Full points will be awarded if the structure meets the given guideline.

Solves Basic Problem [50 points]: Students must ensure that they implement all the requirements in the project documentation. External examiner to confirm if the project solves the given problem. Full points will be awarded if the students include everything asked in the project requirement.

Innovation [20 points]: Students are encouraged to bring new ideas into their development. They can improve the design, use new design patterns, code with a better coding style, or add a feature. External examiner to confirm if the students have added more than the requirement to improve the design or solution. The new addition must include a new feature and should not be similar to the requirements given. Full points will be awarded if the external examiner finds an innovation or see students going beyond the asked requirements.

Best Practices [20 points]: Students must follow the best practices in their development. This will help them to become a quality resource for their prospective employer. External examiner to confirm if the supplied best practices are followed in the project. Full points will be awarded if the best practices are properly implemented.

Performance Consideration [20 points]: Students must consider performance while working on their projects. Performance is one of the critical industry requirements. External examiner to confirm if the student thought the performance improvements in the project. Full points will be awarded if the external examiner sees efforts taken to consider performance aspects in the development.

Security Structure [20 points]: Students need to consider the security aspect If applicable in the design and development. External examiner to confirm if the security consideration is appropriate in this project; if it is applicable, the examiner to verify if the student has considered the security elements in the project. Full points will be awarded if the external examiner sees efforts taken to assess the security aspect of the development.

Benefits of LSET Certificate

Earning the LSET Certificate means you have demonstrated hard-working capabilities and learnt the latest technologies by completing hands-on exercises and real-world projects.

Following are some of the traits employers can trust you have built up through your course;
  • You know how to work in a team environment and communicate well.
  • You know the tools which are necessary for your desired job.
  • You know how to use the latest technologies to develop technologically advanced solutions.
  • You have developed problem-solving skills to navigate complex problem scenarios and find the right solutions.
  • You are now ready to take on the challenge and help your prospective employer to build the desired solutions.
Benefits of LSET Certificate
What to expect after completing the course

What to expect after completing the course?

After earning your certificate from LSET, you can join the LSET’s Alumni club. There are countless benefits associated with the Alumni Club membership. As a member of LSET Alumni, you can expect the following;
  • LSET to hold your hand to find a successful career
  • Advice you on choosing the right job based on your passion and goals
  • Connect you with industry experts for career progression
  • Provide you opportunities to participate in events to keep yourself updated
  • Provide you with a chance to contribute to the game-changing open-source projects
  • Provide you with a platform to shine by allowing you to speak at our events

Tools & Technologies You Will Learn from This Course

Keras

Shogun

Pattern

Theano

Scikit-Learn

Register Now!

Start Your Journey to becoming a Professional Machine Learning Expert

LSET could provide the perfect headstart to start your career in Machine Learning with Python.

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